Figure REF  shows the results of multiple of such searches. First, we optimize backbones on ImageNet in the MobileNetV3 (1.2\(\times \) ) and  DONNA spaces (ours-224), targetting both the number of operations (left) and latency on a simulator targeting tensor compute units. In this case, the input resolution is fixed to \(224\times 224\) . The backbones are first finetuned on ImageNet and then transferred to MS-COCO. Second, we apply the DONNA pipeline directly on the full DONNA-det0 architecture, building an accuracy predictor for MS-COCO. We optimize only the backbone and keep the BiFPN head fixed (Ours-COCO-512). In this case, the resulting networks are directly finetuned on MS-COCO, following the standard DONNA-flow.
For OFA [1]}, we consider two sets of models. The first set consists of models optimized for the number of operations (FLOP) with varying input resolution coming directly from the OFA repository [2]}. The second set of models, which we identify by `OFA-224', are obtained by us with the same tools [2]}, but with the input resolution fixed to \(224\times 224\) . This makes the OFA-224 search space the same as our MobileNetV3 (1.2\(\times \) ) up to the layerwise-vs-blockwise distinction. In the first experiment, we initialize the OFA backbone with weights from progressive shrinking released in [2]}. In the second experiment, we initialize the OFA backbone with from-scratch trained weights on ImageNet using hyperparameters from [5]}. After such initialization, the networks are transferred to object detection for comparison. The comparison of the two experiments shows the benefit of OFA-style training is limited after transfer to a downstream task (See Fig. REF .) The gap between OFA-style training and training from scratch, which is up to \(1.4\%\)  top-1 on ImageNet, decreases to \(0.2\%\)  mAP on COCO, reducing its importance. We discuss this point further in Appendix .
